U.S. patent number 10,365,649 [Application Number 15/522,218] was granted by the patent office on 2019-07-30 for lane curb assisted off-lane checking and lane keeping system for autonomous driving vehicles.
This patent grant is currently assigned to BAIDU.COM TIMES TECHNOLOGY (BEIJING) CO., LTD., BAIDU USA LLC. The grantee listed for this patent is Baidu.com Times Technology (Beijing) Co., Ltd., Baidu USA LLC. Invention is credited to Xiaoxin Fu, Jiarui He, Sen Hu, Qi Kong, Hongye Li, Qi Luo, Yuchang Pan, Jingao Wang, Zhongpu Xia, Guang Yang, Xiang Yu, Chunming Zhao, Fan Zhu, Zhenguang Zhu.
![](/patent/grant/10365649/US10365649-20190730-D00000.png)
![](/patent/grant/10365649/US10365649-20190730-D00001.png)
![](/patent/grant/10365649/US10365649-20190730-D00002.png)
![](/patent/grant/10365649/US10365649-20190730-D00003.png)
![](/patent/grant/10365649/US10365649-20190730-D00004.png)
![](/patent/grant/10365649/US10365649-20190730-D00005.png)
![](/patent/grant/10365649/US10365649-20190730-D00006.png)
![](/patent/grant/10365649/US10365649-20190730-D00007.png)
![](/patent/grant/10365649/US10365649-20190730-D00008.png)
![](/patent/grant/10365649/US10365649-20190730-D00009.png)
![](/patent/grant/10365649/US10365649-20190730-D00010.png)
United States Patent |
10,365,649 |
Zhu , et al. |
July 30, 2019 |
Lane curb assisted off-lane checking and lane keeping system for
autonomous driving vehicles
Abstract
In one embodiment, a lane departure detection system detects at
a first point in time that a wheel of an ADV rolls onto a lane curb
disposed on an edge of a lane in which the ADV is moving. The
system detects at a second point in time that the wheel of the ADV
rolls off the lane curb of the lane. The system calculates an angle
between a moving direction of the ADV and a lane direction of the
lane based on the time difference between the first point in time
and the second point in time in view of a current speed of the ADV.
The system then generates a control command based on the angle to
adjust the moving direction of the ADV in order to prevent the ADV
from further drifting off the lane direction of the lane.
Inventors: |
Zhu; Fan (Sunnyvale, CA),
Kong; Qi (Sunnyvale, CA), Luo; Qi (Sunnyvale, CA),
Yu; Xiang (Sunnyvale, CA), Hu; Sen (Sunnyvale, CA),
Zhu; Zhenguang (Beijing, CN), Fu; Xiaoxin
(Beijing, CN), He; Jiarui (Beijing, CN),
Li; Hongye (Beijing, CN), Pan; Yuchang (Beijing,
CN), Xia; Zhongpu (Beijing, CN), Zhao;
Chunming (Beijing, CN), Yang; Guang (San Jose,
CA), Wang; Jingao (Sunnyvale, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Baidu USA LLC
Baidu.com Times Technology (Beijing) Co., Ltd. |
Sunnyvale
Beijing |
CA
N/A |
US
CN |
|
|
Assignee: |
BAIDU USA LLC (Sunnyvale,
CA)
BAIDU.COM TIMES TECHNOLOGY (BEIJING) CO., LTD. (Beijing,
CN)
|
Family
ID: |
63854478 |
Appl.
No.: |
15/522,218 |
Filed: |
April 19, 2017 |
PCT
Filed: |
April 19, 2017 |
PCT No.: |
PCT/CN2017/081057 |
371(c)(1),(2),(4) Date: |
April 26, 2017 |
PCT
Pub. No.: |
WO2018/191881 |
PCT
Pub. Date: |
October 25, 2018 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20180307234 A1 |
Oct 25, 2018 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05D
1/0088 (20130101); G05D 1/0246 (20130101); B60W
50/02 (20130101); G05D 1/0212 (20130101); B60W
60/0018 (20200201); B60W 30/12 (20130101); B60W
2420/40 (20130101); B60W 2554/60 (20200201); G05D
2201/0213 (20130101); B60W 2530/20 (20130101); B60W
2422/70 (20130101); B60W 2050/0215 (20130101) |
Current International
Class: |
G05D
1/00 (20060101); G05D 1/02 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Zanelli; Michael J
Attorney, Agent or Firm: Womble Bond Dickinson (US) LLP
Claims
What is claimed is:
1. A computer-implemented method for operating an autonomous
driving vehicle, the method comprising: detecting, a first point in
time, that a wheel of an autonomous driving vehicle (ADV) rolls
onto a lane curb disposed on an edge of a lane in which the ADV is
moving; detecting, at a second point in time, that the wheel of the
ADV rolls off the lane curb of the lane; calculating an angle
between a moving direction of the ADV and a lane direction of the
lane based on a difference between the first point in time and the
second point in time in view of a current speed of the ADV,
including determining a curb width of the lane curb based on
perception data that perceives the lane curb, calculating a
distance perpendicular to the lane direction of the lane that the
ADV moves from the first point in time and the second point in time
based on the curb width and a wheel width of the wheel determined
based on a specification of the wheel, and determining the angle
based on the distance and the current speed of the ADV in view of
the difference between the first point in time and the second point
in time; and generating a control command based on the angle to
adjust the moving direction of the ADV to prevent the ADV from
further drifting off the lane direction of the lane.
2. The method of claim 1, further comprising determining whether
the angle is above a predetermined threshold, wherein the control
command is generated if the angle is above the predetermined
threshold.
3. The method of claim 2, wherein the predetermined threshold is
determined based on a lane configuration of the lane.
4. The method of claim 1, wherein the perception data perceiving
the lane curb includes one or more images of the lane curb captured
by one or more cameras.
5. The method of claim 1, wherein the wheel contacting the lane
curb is detected via a tire pressure sensor or a motion sensor
disposed near the wheel.
6. A non-transitory machine-readable medium having instructions
stored therein, which when executed by a processor, cause the
processor to perform operations of operating an autonomous driving
vehicle, the operations comprising: detecting, a first point in
time, that a wheel of an autonomous driving vehicle (ADV) rolls
onto a lane curb disposed on an edge of a lane in which the ADV is
moving; detecting, at a second point in time, that the wheel of the
ADV rolls off the lane curb of the lane; calculating an angle
between a moving direction of the ADV and a lane direction of the
lane based on a difference between the first point in time and the
second point in time in view of a current speed of the ADV,
including determining a curb width of the lane curb based on
perception data that perceives the lane curb, calculating a
distance perpendicular to the lane direction of the lane that the
ADV moves from the first point in time and the second point in time
based on the curb width and a wheel width of the wheel determined
based on a specification of the wheel, and determining the angle
based on the distance and the current speed of the ADV in view of
the difference between the first point in time and the second point
in time; and generating a control command based on the angle to
adjust the moving direction of the ADV to prevent the ADV from
further drifting off the lane direction of the lane.
7. The machine-readable medium of claim 6, wherein the operations
further comprise determining whether the angle is above a
predetermined threshold, wherein the control command is generated
if the angle is above the predetermined threshold.
8. The machine-readable medium of claim 7, wherein the
predetermined threshold is determined based on a lane configuration
of the lane.
9. The machine-readable medium of claim 6, wherein the perception
data perceiving the lane curb includes one or more images of the
lane curb captured by one or more cameras.
10. The machine-readable medium of claim 6, wherein the wheel
contacting the lane curb is detected via a tire pressure sensor or
a motion sensor disposed near the wheel.
11. A data processing system, comprising: a processor; and a memory
coupled to the processor to store instructions, which when executed
by the processor, cause the processor to perform operations, the
operations including detecting, a first point in time, that a wheel
of an autonomous driving vehicle (ADV) rolls onto a lane curb
disposed on an edge of a lane in which the ADV is moving,
detecting, at a second point in time, that the wheel of the ADV
rolls off the lane curb of the lane, calculating an angle between a
moving direction of the ADV and a lane direction of the lane based
on a difference between the first point in time and the second
point in time in view of a current speed of the ADV, including
determining a curb width of the lane curb based on perception data
that perceives the lane curb, calculating a distance perpendicular
to the lane direction of the lane that the ADV moves from the first
point in time and the second point in time based on the curb width
and a wheel width of the wheel determined based on a specification
of the wheel, and determining the angle based on the distance and
the current speed of the ADV in view of the difference between the
first point in time and the second point in time, and generating a
control command based on the angle to adjust the moving direction
of the ADV to prevent the ADV from further drifting off the lane
direction of the lane.
12. The system of claim 11, wherein the operations further comprise
determining whether the angle is above a predetermined threshold,
wherein the control command is generated if the angle is above the
predetermined threshold.
13. The system of claim 12, wherein the predetermined threshold is
determined based on a lane configuration of the lane.
14. The system of claim 11, wherein the perception data perceiving
the lane curb includes one or more images of the lane curb captured
by one or more cameras.
15. A computer-implemented method for operating an autonomous
driving vehicle, the method comprising: detecting, a first point in
time, that a first wheel of an autonomous driving vehicle (ADV)
contacts a lane curb disposed on an edge of a lane in which the ADV
is moving; detecting, at a second point in time, that a second
wheel of the ADV contacts the lane curb of the lane; calculating an
angle between a moving direction of the ADV and a lane direction of
the lane based on a difference between the first point in time and
the second point in time in view of a current speed of the ADV; and
generating a control command based on the angle to adjust the
moving direction of the ADV to prevent the ADV from further
drifting off the lane direction of the lane.
16. The method of claim 15, wherein calculating an angle between a
moving direction of the ADV and a lane direction of the lane
comprises: determining a first distance between the first wheel and
the second wheel; and determining a second distance that the ADV
has moved perpendicular to the lane direction of the lane based on
the difference between the first point in time and the second point
in time in view of the current speed of the ADV, wherein the angle
is calculated based on the first distance and the second
distance.
17. The method of claim 16, wherein the angle is calculated based
on a sinusoidal relationship between the first distance and the
second distance.
18. The method of claim 15, wherein the first wheel and the second
wheel of the ADV is coupled to each to via an axle.
19. The method of claim 15, wherein the first wheel contacting the
lane curb is detected via a first tire pressure sensor or a first
motion sensor associated with the first wheel.
20. The method of claim 19, wherein the second wheel contacting the
lane curb is detected via a second tire pressure sensor or a second
motion sensor associated with the second wheel.
21. A non-transitory machine-readable medium having instructions
stored therein, which when executed by a processor, cause the
processor to perform operations of operating an autonomous driving
vehicle, the operations comprising: detecting, a first point in
time, that a first wheel of an autonomous driving vehicle (ADV)
contacts a lane curb disposed on an edge of a lane in which the ADV
is moving; detecting, at a second point in time, that a second
wheel of the ADV contacts the lane curb of the lane; calculating an
angle between a moving direction of the ADV and a lane direction of
the lane based on a difference between the first point in time and
the second point in time in view of a current speed of the ADV; and
generating a control command based on the angle to adjust the
moving direction of the ADV to prevent the ADV from further
drifting off the lane direction of the lane.
22. The machine-readable medium of claim 21, wherein calculating an
angle between a moving direction of the ADV and a lane direction of
the lane comprises: determining a first distance between the first
wheel and the second wheel; and determining a second distance that
the ADV has moved perpendicular to the lane direction of the lane
based on the difference between the first point in time and the
second point in time in view of the current speed of the ADV,
wherein the angle is calculated based on the first distance and the
second distance.
23. The machine-readable medium of claim 22, wherein the angle is
calculated based on a sinusoidal relationship between the first
distance and the second distance.
24. The machine-readable medium of claim 21, wherein the first
wheel and the second wheel of the ADV is coupled to each to via an
axle.
25. The machine-readable medium of claim 21, wherein the first
wheel contacting the lane curb is detected via a first tire
pressure sensor or a first motion sensor associated with the first
wheel.
26. The machine-readable medium of claim 25, wherein the second
wheel contacting the lane curb is detected via a second tire
pressure sensor or a second motion sensor associated with the
second wheel.
Description
CROSS-REFERENCE TO RELATED APPLICATION
This patent application is a U.S. National Phase Application under
35 U.S.C. .sctn. 371 of International Application No.
PCT/CN2017/081057, filed Apr. 19, 2017, entitled "LANE CURB
ASSISTED OFF-LANE CHECKING AND LANE KEEPING SYSTEM FOR AUTONOMOUS
DRIVING VEHICLES," which is incorporated by reference herein by its
entirety.
TECHNICAL FIELD
Embodiments of the present invention relate generally to operating
autonomous vehicles. More particularly, embodiments of the
invention relate to lane departure detection based on lane curb
sensing.
BACKGROUND
Vehicles operating in an autonomous mode (e.g., driverless) can
relieve occupants, especially the driver, from some driving-related
responsibilities. When operating in an autonomous mode, the vehicle
can navigate to various locations using onboard sensors, allowing
the vehicle to travel with minimal human interaction or in some
cases without any passengers.
Motion planning and control are critical operations in autonomous
driving. It is important for an autonomous driving vehicle (ADV) to
drive and remain within a lane in which the ADV is moving. However,
it is possible that the perception or planning of autonomous
driving could be inaccurate and do not detect that the ADV does not
follow the lane correctly. It is difficult to detect such a
scenario, especially when the lane is not painted in contrast
enough.
SUMMARY
Embodiments of the present disclosure provide a
computer-implemented method for operating an autonomous driving
vehicle, a non-transitory machine-readable medium, and a data
processing system.
In an aspect of the disclosure, the computer-implemented method for
operating an autonomous driving vehicle comprises: detecting, a
first point in time, that a wheel of an autonomous driving vehicle
(ADV) rolls onto a lane curb disposed on an edge of a lane in which
the ADV is moving; detecting, at a second point in time, that the
wheel of the ADV rolls off the lane curb of the lane; calculating
an angle between a moving direction of the ADV and a lane direction
of the lane based on a difference between the first point in time
and the second point in time in view of a current speed of the ADV;
and generating a control command based on the angle to adjust the
moving direction of the ADV to prevent the ADV from further
drifting off the lane direction of the lane.
In another aspect of the disclosure, the non-transitory
machine-readable medium has instructions stored therein, which when
executed by a processor, cause the processor to perform operations
of operating an autonomous driving vehicle, the operations
comprising: detecting, a first point in time, that a wheel of an
autonomous driving vehicle (ADV) rolls onto a lane curb disposed on
an edge of a lane in which the ADV is moving; detecting, at a
second point in time, that the wheel of the ADV rolls off the lane
curb of the lane; calculating an angle between a moving direction
of the ADV and a lane direction of the lane based on a difference
between the first point in time and the second point in time in
view of a current speed of the ADV; and generating a control
command based on the angle to adjust the moving direction of the
ADV to prevent the ADV from further drifting off the lane direction
of the lane.
In a further aspect of the disclosure, the data processing system
comprises a processor; and a memory coupled to the processor to
store instructions, which when executed by the processor, cause the
processor to perform operations, the operations including:
detecting, a first point in time, that a wheel of an autonomous
driving vehicle (ADV) rolls onto a lane curb disposed on an edge of
a lane in which the ADV is moving, detecting, at a second point in
time, that the wheel of the ADV rolls off the lane curb of the
lane, calculating an angle between a moving direction of the ADV
and a lane direction of the lane based on a difference between the
first point in time and the second point in time in view of a
current speed of the ADV, and generating a control command based on
the angle to adjust the moving direction of the ADV to prevent the
ADV from further drifting off the lane direction of the lane.
In a further aspect of the disclosure, the computer-implemented
method for operating an autonomous driving vehicle comprises:
detecting, a first point in time, that a first wheel of an
autonomous driving vehicle (ADV) contacts a lane curb disposed on
an edge of a lane in which the ADV is moving; detecting, at a
second point in time, that a second wheel of the ADV contacts the
lane curb of the lane; calculating an angle between a moving
direction of the ADV and a lane direction of the lane based on a
difference between the first point in time and the second point in
time in view of a current speed of the ADV; and generating a
control command based on the angle to adjust the moving direction
of the ADV to prevent the ADV from further drifting off the lane
direction of the lane.
BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments of the invention are illustrated by way of example and
not limitation in the figures of the accompanying drawings in which
like references indicate similar elements.
FIG. 1 is a block diagram illustrating a networked system according
to one embodiment of the invention.
FIG. 2 is a block diagram illustrating an example of an autonomous
vehicle according to one embodiment of the invention.
FIG. 3 is a block diagram illustrating an example of a perception
and planning system used with an autonomous vehicle according to
one embodiment of the invention.
FIG. 4 is a processing flow diagram illustrating a processing flow
of detecting and correcting lane departure of an autonomous driving
vehicle according to one embodiment of the invention.
FIG. 5 is a diagram illustrating a typical scenario when a vehicle
contacts a lane curb.
FIG. 6 is a diagram for determining a difference between a moving
direction and a lane direction according to one embodiment of the
invention.
FIG. 7 is a flow diagram illustrating a process of operating an
autonomous driving vehicle according to one embodiment of the
invention.
FIG. 8 is a diagram for determining a difference between a moving
direction and a lane direction according to another embodiment of
the invention.
FIG. 9 is a flow diagram illustrating a process of operating an
autonomous driving vehicle according to another embodiment of the
invention.
FIG. 10 is a block diagram illustrating a data processing system
according to one embodiment.
DETAILED DESCRIPTION
Various embodiments and aspects of the inventions will be described
with reference to details discussed below, and the accompanying
drawings will illustrate the various embodiments. The following
description and drawings are illustrative of the invention and are
not to be construed as limiting the invention. Numerous specific
details are described to provide a thorough understanding of
various embodiments of the present invention. However, in certain
instances, well-known or conventional details are not described in
order to provide a concise discussion of embodiments of the present
inventions.
Reference in the specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in conjunction with the embodiment can be
included in at least one embodiment of the invention. The
appearances of the phrase "in one embodiment" in various places in
the specification do not necessarily all refer to the same
embodiment.
According to some embodiments, a lane departure detection system is
configured to detect that an ADV is departing from the lane in
which the ADV is driving based on sensor data captured when the ADV
contacts a lane curb disposed on the edge of the lane, either on
the shoulder of the lane or between lanes. When the ADV contacts
the lane curb, the lane departure detection system detects and
calculates an angle between a moving direction of the ADV and a
lane direction of the lane based on timing of the contacts in view
of the speed of the ADV. Based on the angle, the system calculates
how much the moving direction of the ADV is off compared to a lane
direction of the lane. The lane direction is typically
substantially in parallel with a longitudinal axis or direction of
a lane curb or a distribution line or pattern of an array of lane
curb segments of the lane curb disposed on an edge of a lane or
between lanes. A control command such as a speed control command
and/or a steering control command is generated based on the angle
and/or the distance that ADV is off from the lane to correct the
moving direction of the ADV.
In one aspect of the invention, a lane departure detection system
detects at a first point in time that a wheel of an ADV rolls onto
a lane curb disposed on an edge of a lane in which the ADV is
moving. The system detects at a second point in time that the same
wheel of the ADV rolls off the lane curb of the lane. The contacts
between the wheel rolling on and rolling off is detected using a
sensor associated with the wheel such as a tire pressure sensor or
a motion sensor. The wheel can be any one of the wheels of the ADV,
either being a front wheel or a rear wheel. The system calculates
an angle between a moving direction of the ADV and a lane direction
of the lane based on the time difference between the first point in
time and the second point in time in view of a current speed of the
ADV. The system then generates a control command (e.g., speed
control command, steering control command) based on the angle to
adjust the moving direction of the ADV in order to prevent the ADV
from further drifting off the lane direction of the lane.
In calculating the angle, according to one embodiment, a distance
perpendicular to the lane direction of the lane is calculated from
the first point in time to the second point in time. Such a
distance is also referred to as a lateral moving distance of the
ADV. The angle is then calculated based on the distance and the
current speed of the ADV in view of the time difference between the
first point in time and the second point in time. The distance
perpendicular to the lane direction may be calculated based on a
wheel width of the wheel and a curb width of the lane curb. The
wheel width may be determined based on the specification of the
wheel. Curb width of the lane curb may be determined based on the
perception data perceiving the lane curb such as an image of the
lane captured by a camera.
According to another aspect of the invention, a lane departure
detection system detects at a first point in time that a first
wheel of an ADV contacts a lane curb disposed on an edge of a lane
in which the ADV is moving. The system detects at a second point in
time that a second wheel of the ADV contacts the lane curb of the
lane. The contact between the first wheel and the lane curb is
detected using a sensor associated with the first wheel such as a
tire pressure sensor or a motion sensor. The contact between the
second wheel and the lane curb is detected using a sensor
associated with the first wheel such as a tire pressure sensor or a
motion sensor. The first wheel and the second wheel are different
wheels, which can be any of the wheels of the ADV such as a pair of
front wheels or rear wheels. The system calculates an angle between
a moving direction of the ADV and a lane direction of the lane
based on the time difference between the first point in time and
the second point in time in view of a current speed of the ADV. The
system then generates a control command (e.g., speed control
command, steering control command) based on the angle to adjust the
moving direction of the ADV in order to prevent the ADV from
further drifting off the lane direction of the lane.
In calculating the angle, according to one embodiment, a first
distance between the first wheel and the second wheel is
determined. The first distance can be the length of an axle
coupling the first wheel and the second wheel. A second distance
that the ADV has moved perpendicular to the lane direction of the
lane (e.g., lateral moving distance) is determined based on the
time difference between the first point in time and the second
point in time in view of the current speed of the ADV. The angle is
then calculated based on a sinusoidal relationship between the
first distance and the second distance.
FIG. 1 is a block diagram illustrating an autonomous vehicle
network configuration according to one embodiment of the invention.
Referring to FIG. 1, network configuration 100 includes autonomous
vehicle 101 that may be communicatively coupled to one or more
servers 103-104 over a network 102. Although there is one
autonomous vehicle shown, multiple autonomous vehicles can be
coupled to each other and/or coupled to servers 103-104 over
network 102. Network 102 may be any type of networks such as a
local area network (LAN), a wide area network (WAN) such as the
Internet, a cellular network, a satellite network, or a combination
thereof, wired or wireless. Server(s) 103-104 may be any kind of
servers or a cluster of servers, such as Web or cloud servers,
application servers, backend servers, or a combination thereof.
Servers 103-104 may be data analytics servers, content servers,
traffic information servers, map and point of interest (MPOI)
severs, or location servers, etc.
An autonomous vehicle refers to a vehicle that can be configured to
in an autonomous mode in which the vehicle navigates through an
environment with little or no input from a driver. Such an
autonomous vehicle can include a sensor system having one or more
sensors that are configured to detect information about the
environment in which the vehicle operates. The vehicle and its
associated controller(s) use the detected information to navigate
through the environment. Autonomous vehicle 101 can operate in a
manual mode, a full autonomous mode, or a partial autonomous
mode.
In one embodiment, autonomous vehicle 101 includes, but is not
limited to, perception and planning system 110, vehicle control
system 111, wireless communication system 112, user interface
system 113, infotainment system 114, and sensor system 115.
Autonomous vehicle 101 may further include certain common
components included in ordinary vehicles, such as, an engine,
wheels, steering wheel, transmission, etc., which may be controlled
by vehicle control system 111 and/or perception and planning system
110 using a variety of communication signals and/or commands, such
as, for example, acceleration signals or commands, deceleration
signals or commands, steering signals or commands, braking signals
or commands, etc.
Components 110-115 may be communicatively coupled to each other via
an interconnect, a bus, a network, or a combination thereof. For
example, components 110-115 may be communicatively coupled to each
other via a controller area network (CAN) bus. A CAN bus is a
vehicle bus standard designed to allow microcontrollers and devices
to communicate with each other in applications without a host
computer. It is a message-based protocol, designed originally for
multiplex electrical wiring within automobiles, but is also used in
many other contexts.
Referring now to FIG. 2, in one embodiment, sensor system 115
includes, but it is not limited to, one or more cameras 211, global
positioning system (GPS) unit 212, inertial measurement unit (IMU)
213, radar unit 214, and a light detection and range (LIDAR) unit
215. GPS system 212 may include a transceiver operable to provide
information regarding the position of the autonomous vehicle. IMU
unit 213 may sense position and orientation changes of the
autonomous vehicle based on inertial acceleration. Radar unit 214
may represent a system that utilizes radio signals to sense objects
within the local environment of the autonomous vehicle. In some
embodiments, in addition to sensing objects, radar unit 214 may
additionally sense the speed and/or heading of the objects. LIDAR
unit 215 may sense objects in the environment in which the
autonomous vehicle is located using lasers. LIDAR unit 215 could
include one or more laser sources, a laser scanner, and one or more
detectors, among other system components. Cameras 211 may include
one or more devices to capture images of the environment
surrounding the autonomous vehicle. Cameras 211 may be still
cameras and/or video cameras. A camera may be mechanically movable,
for example, by mounting the camera on a rotating and/or tilting a
platform.
Sensor system 115 may further include other sensors, such as, a
sonar sensor, an infrared sensor, a steering sensor, a throttle
sensor, a braking sensor, and an audio sensor (e.g., microphone).
An audio sensor may be configured to capture sound from the
environment surrounding the autonomous vehicle. A steering sensor
may be configured to sense the steering angle of a steering wheel,
wheels of the vehicle, or a combination thereof. A throttle sensor
and a braking sensor sense the throttle position and braking
position of the vehicle, respectively. In some situations, a
throttle sensor and a braking sensor may be integrated as an
integrated throttle/braking sensor.
In one embodiment, sensor system 115 further includes one or more
tire pressure sensors 216 and/or one or more motion sensors 217.
Each of tire pressure sensors 216 is configured to sense and
measure a tire pressure of one of the wheels of the vehicle. In one
embodiment, each of the wheels of the ADV is associated with a tire
pressure sensor and/or a motion sensor. Such sensors may be
disposed or mounted near the corresponding wheel, for example, near
a suspension joint associated with the wheel. Thus, when a wheel of
the ADV contacts a lane curb, it can be precisely determined which
of the wheels of the ADV contacts the lane curb. It can also detect
whether the wheel is rolling onto or engaging with the lane curb or
is rolling off or disengaging from the lane curb.
The sudden change of the tire pressure of a wheel proportionally
represents the impact imposed on the wheel when the wheel contacts
a lane curb or rolls on and/or off the lane curb. Each of the
motion sensors 217 is configured to sense and measure an amount of
motion incurred by a wheel or the ADV. The amount of sudden motion
detected may be utilized to determine whether the ADV contacts a
lane curb or rolls on and/or off the lane curb. In one embodiment,
a motion sensor may be positioned near each wheel or a suspension
joint associated with each wheel. The tire pressure data and the
motion sensor data may be combined to determine whether the
corresponding wheel has contacted a lane curb or rolls on and/or
off the lane curb.
In one embodiment, vehicle control system 111 includes, but is not
limited to, steering unit 201, throttle unit 202 (also referred to
as an acceleration unit), and braking unit 203. Steering unit 201
is to adjust the direction or heading of the vehicle. Throttle unit
202 is to control the speed of the motor or engine that in turn
control the speed and acceleration of the vehicle. Braking unit 203
is to decelerate the vehicle by providing friction to slow the
wheels or tires of the vehicle. Note that the components as shown
in FIG. 2 may be implemented in hardware, software, or a
combination thereof.
Referring back to FIG. 1, wireless communication system 112 is to
allow communication between autonomous vehicle 101 and external
systems, such as devices, sensors, other vehicles, etc. For
example, wireless communication system 112 can wirelessly
communicate with one or more devices directly or via a
communication network, such as servers 103-104 over network 102.
Wireless communication system 112 can use any cellular
communication network or a wireless local area network (WLAN),
e.g., using WiFi to communicate with another component or system.
Wireless communication system 112 could communicate directly with a
device (e.g., a mobile device of a passenger, a display device, a
speaker within vehicle 101), for example, using an infrared link,
Bluetooth, etc. User interface system 113 may be part of peripheral
devices implemented within vehicle 101 including, for example, a
keyword, a touch screen display device, a microphone, and a
speaker, etc.
Some or all of the functions of autonomous vehicle 101 may be
controlled or managed by perception and planning system 110,
especially when operating in an autonomous driving mode. Perception
and planning system 110 includes the necessary hardware (e.g.,
processor(s), memory, storage) and software (e.g., operating
system, planning and routing programs) to receive information from
sensor system 115, control system 111, wireless communication
system 112, and/or user interface system 113, process the received
information, plan a route or path from a starting point to a
destination point, and then drive vehicle 101 based on the planning
and control information. Alternatively, perception and planning
system 110 may be integrated with vehicle control system 111.
For example, a user as a passenger may specify a starting location
and a destination of a trip, for example, via a user interface.
Perception and planning system 110 obtains the trip related data.
For example, perception and planning system 110 may obtain location
and route information from an MPOI server, which may be a part of
servers 103-104. The location server provides location services and
the MPOI server provides map services and the POIs of certain
locations. Alternatively, such location and MPOI information may be
cached locally in a persistent storage device of perception and
planning system 110.
While autonomous vehicle 101 is moving along the route, perception
and planning system 110 may also obtain real-time traffic
information from a traffic information system or server (TIS). Note
that servers 103-104 may be operated by a third party entity.
Alternatively, the functionalities of servers 103-104 may be
integrated with perception and planning system 110. Based on the
real-time traffic information, MPOI information, and location
information, as well as real-time local environment data detected
or sensed by sensor system 115 (e.g., obstacles, objects, nearby
vehicles), perception and planning system 110 can plan an optimal
route and drive vehicle 101, for example, via control system 111,
according to the planned route to reach the specified destination
safely and efficiently.
Server 103 may be a data analytics system to perform data analytics
services for a variety of clients. In one embodiment, data
analytics system 103 includes data collector 121 and machine
learning engine 122. Data collector 121 collects driving statistics
123 from a variety of vehicles, either autonomous vehicles or
regular vehicles driven by human drivers. Driving statistics 123
include information indicating the driving commands (e.g.,
throttle, brake, steering commands) issued and responses of the
vehicles (e.g., speeds, accelerations, decelerations, directions)
captured by sensors of the vehicles at different points in time.
Driving statistics 123 may further include information describing
the driving environments at different points in time, such as, for
example, routes (including starting and destination locations),
MPOIs, road conditions, weather conditions, etc.
Based on driving statistics 123, machine learning engine 122
generates or trains a set of rules, algorithms, and/or predictive
models 124 for a variety of purposes. In one embodiment, algorithms
124 include an algorithm to calculate angle between a moving
direction of an ADV and a lane direction of a lane which the ADV is
moving. The angle may be calculated in view of a physical dimension
of the ADV (e.g., distance between two front or rear wheels,
distance between a front wheel and a rear wheel). Such an angle is
utilized to determine whether the ADV is departing from the lane
and an appropriate control action can be taken to correct such lane
departure. Algorithms 124 are then uploaded onto an ADV to be
utilized in real-time to detect the potential lane departure.
FIG. 3 is a block diagram illustrating an example of a perception
and planning system used with an autonomous vehicle according to
one embodiment of the invention. System 300 may be implemented as a
part of autonomous vehicle 101 of FIG. 1 including, but is not
limited to, perception and planning system 110, control system 111,
and sensor system 115. Referring to FIG. 3, perception and planning
system 110 includes, but is not limited to, localization module
301, perception module 302, decision module 303, planning module
304, control module 305, and lane departure detector or monitor
306.
Some or all of modules 301-306 may be implemented in software,
hardware, or a combination thereof. For example, these modules may
be installed in persistent storage device 352, loaded into memory
351, and executed by one or more processors (not shown). Note that
some or all of these modules may be communicatively coupled to or
integrated with some or all modules of vehicle control system 111
of FIG. 2. Some of modules 301-306 may be integrated together as an
integrated module.
Localization module 301 determines a current location of autonomous
vehicle 300 (e.g., leveraging GPS unit 212) and manages any data
related to a trip or route of a user. Localization module 301 (also
referred to as a map and route module) manages any data related to
a trip or route of a user. A user may log in and specify a starting
location and a destination of a trip, for example, via a user
interface. Localization module 301 communicates with other
components of autonomous vehicle 300, such as map and route
information 311, to obtain the trip related data. For example,
localization module 301 may obtain location and route information
from a location server and a map and POI (MPOI) server. A location
server provides location services and an MPOI server provides map
services and the POIs of certain locations, which may be cached as
part of map and route information 311. While autonomous vehicle 300
is moving along the route, localization module 301 may also obtain
real-time traffic information from a traffic information system or
server.
Based on the sensor data provided by sensor system 115 and
localization information obtained by localization module 301, a
perception of the surrounding environment is determined by
perception module 302. The perception information may represent
what an ordinary driver would perceive surrounding a vehicle in
which the driver is driving. The perception can include the lane
configuration (e.g., straight or curve lanes), traffic light
signals, a relative position of another vehicle, a pedestrian, a
building, crosswalk, or other traffic related signs (e.g., stop
signs, yield signs), etc., for example, in a form of an object.
Perception module 302 may include a computer vision system or
functionalities of a computer vision system to process and analyze
images captured by one or more cameras in order to identify objects
and/or features in the environment of autonomous vehicle. The
objects can include traffic signals, road way boundaries, other
vehicles, pedestrians, and/or obstacles, etc. The computer vision
system may use an object recognition algorithm, video tracking, and
other computer vision techniques. In some embodiments, the computer
vision system can map an environment, track objects, and estimate
the speed of objects, etc. Perception module 302 can also detect
objects based on other sensors data provided by other sensors such
as a radar and/or LIDAR.
For each of the objects, decision module 303 makes a decision
regarding how to handle the object. For example, for a particular
object (e.g., another vehicle in a crossing route) as well as its
metadata describing the object (e.g., a speed, direction, turning
angle), decision module 303 decides how to encounter the object
(e.g., overtake, yield, stop, pass). Decision module 303 may make
such decisions according to a set of rules such as traffic rules or
driving rules 312, which may be stored in persistent storage device
352.
Based on a decision for each of the objects perceived, planning
module 304 plans a path or route for the autonomous vehicle, as
well as driving parameters (e.g., distance, speed, and/or turning
angle). That is, for a given object, decision module 303 decides
what to do with the object, while planning module 304 determines
how to do it. For example, for a given object, decision module 303
may decide to pass the object, while planning module 304 may
determine whether to pass on the left side or right side of the
object. Planning and control data is generated by planning module
304 including information describing how vehicle 300 would move in
a next moving cycle (e.g., next route/path segment). For example,
the planning and control data may instruct vehicle 300 to move 10
meters at a speed of 30 mile per hour (mph), then change to a right
lane at the speed of 25 mph.
Based on the planning and control data, control module 305 controls
and drives the autonomous vehicle, by sending proper commands or
signals to vehicle control system 111, according to a route or path
defined by the planning and control data. The planning and control
data include sufficient information to drive the vehicle from a
first point to a second point of a route or path using appropriate
vehicle settings or driving parameters (e.g., throttle, braking,
and turning commands) at different points in time along the path or
route.
In one embodiment, the planning phase is performed in a number of
planning cycles, also referred to as command cycles, such as, for
example, in every time interval of 100 milliseconds (ms). For each
of the planning cycles or command cycles, one or more control
commands will be issued based on the planning and control data.
That is, for every 100 ms, planning module 304 plans a next route
segment or path segment, for example, including a target position
and the time required for the ADV to reach the target position.
Alternatively, planning module 304 may further specify the specific
speed, direction, and/or steering angle, etc. In one embodiment,
planning module 304 plans a route segment or path segment for the
next predetermined period of time such as 5 seconds. For each
planning cycle, planning module 304 plans a target position for the
current cycle (e.g., next 5 seconds) based on a target position
planned in a previous cycle. If the current actual position of the
ADV is significantly different from the target position planned by
a previous planning cycle, planning module 304 may have to replan
the next segment based on the actual position of the ADV instead of
the target position of the previous planning cycle. Control module
305 then generates one or more control commands (e.g., throttle,
brake, steering control commands) based on the planning and control
data of the current cycle.
Note that decision module 303 and planning module 304 may be
integrated as an integrated module. Decision module 303/planning
module 304 may include a navigation system or functionalities of a
navigation system to determine a driving path for the autonomous
vehicle. For example, the navigation system may determine a series
of speeds and directional headings to effect movement of the
autonomous vehicle along a path that substantially avoids perceived
obstacles while generally advancing the autonomous vehicle along a
roadway-based path leading to an ultimate destination. The
destination may be set according to user inputs via user interface
system 113. The navigation system may update the driving path
dynamically while the autonomous vehicle is in operation. The
navigation system can incorporate data from a GPS system and one or
more maps so as to determine the driving path for the autonomous
vehicle.
Decision module 303/planning module 304 may further include a
collision avoidance system or functionalities of a collision
avoidance system to identify, evaluate, and avoid or otherwise
negotiate potential obstacles in the environment of the autonomous
vehicle. For example, the collision avoidance system may effect
changes in the navigation of the autonomous vehicle by operating
one or more subsystems in control system 111 to undertake swerving
maneuvers, turning maneuvers, braking maneuvers, etc. The collision
avoidance system may automatically determine feasible obstacle
avoidance maneuvers on the basis of surrounding traffic patterns,
road conditions, etc. The collision avoidance system may be
configured such that a swerving maneuver is not undertaken when
other sensor systems detect vehicles, construction barriers, etc.
in the region adjacent the autonomous vehicle that would be swerved
into. The collision avoidance system may automatically select the
maneuver that is both available and maximizes safety of occupants
of the autonomous vehicle. The collision avoidance system may
select an avoidance maneuver predicted to cause the least amount of
acceleration in a passenger cabin of the autonomous vehicle.
Lane departure detector or detection module 306 is configured to
detect whether the ADV is departing from or drifting off a lane in
which the ADV is moving. In one embodiment, lane departure detector
306 is coupled to one or more sensors such as tire pressure sensors
216 and/or motion sensors 217 of FIG. 2 to detect or sense whether
the ADV experiences sudden bump or oscillation, for example, in
response to contacting a lane curb disposed on an edge of the lane
such as a lane shoulder, a lane warning area of a lane, or a lane
separator between lanes. In response to such sudden bump or
oscillation, lane departure detector 306 determines an angle
between a moving direction of the ADV and a lane direction of the
lane at the point in time. The angle represents how much the moving
direction of the ADV is off compared to the lane direction of the
lane (e.g., difference between the moving direction and the lane
direction). Based on the angle, planning module 304 and/or control
module 305 can decide whether a correction of moving direction is
warranted and if so, a new control command is generated and issued
to the ADV to correct the moving direction of the ADV.
In one embodiment, the correction of moving direction of the ADV is
needed if the angle representing the difference between the moving
direction and the lane direction is greater than a predetermined
threshold. The predetermined threshold may be determined and
configured by a data analytics system (e.g., data analytics system
103) offline based on a large amount of driving statistics
collected over a period of time from a variety of vehicles. Such a
predetermined threshold may be determined in consideration of
safety reasons and/or human drivers' driving behaviors or
preferences (e.g., comfort reasons).
According to one embodiment, lane departure detector 306 includes
motion detector or detection module 321 and angle calculator 322.
Lane departure detector 306 is configured to detect that an ADV is
departing from the lane in which the ADV is driving based on sensor
data captured when the ADV contact a lane curb. When the ADV
contacts the lane curb, motion detector 321 of lane departure
detector 306 detects such a sudden motion (e.g., bump, oscillation)
via tire pressure sensors and/or motion sensors. Angle calculator
322 calculates an angle of a moving direction of the ADV vs a
longitudinal direction of the lane curb.
In one embodiment, the angle may be calculated based on the timing
when a wheel of the ADV rolls onto the lane curb and the timing
when the wheel of the ADV rolls off the lane curb in view of the
speed of the ADV. Alternatively, the angle may be calculated based
on the timing when a first wheel (e.g., right front wheel) of the
ADV contacts the lane curb and the timing when a second wheel
(e.g., left front wheel) of the ADV contacts the lane curb in view
of the speed of the ADV. Based on the angle, lane departure
detector 306 calculates how much the moving direction of the ADV is
off compared to a lane direction of the lane. The lane direction is
typically substantially parallel with the longitudinal direction of
the lane curb. A control command such as a speed control command
and/or a steering control command is generated by planning module
304 and/or control module 305 based on the angle to correct the
moving direction of the ADV.
FIG. 4 is a processing flow diagram illustrating a processing flow
of detecting and correcting lane departure of an autonomous driving
vehicle according to one embodiment of the invention. Referring to
FIGS. 3 and 4, as described above, based on perception data
received from perception module 303, planning module 304 plans a
route segment and specifies a target position and the time to be at
the target position, etc. Based on the planning and control data
provided by planning module 304, control module 305 determines the
necessary control command or commands (e.g., speed control command,
steering control command) and issues the control commands to
vehicle platform 405.
In addition, lane departure detector 306 is coupled to vehicle
platform 405 such as tire pressure sensors 216 and/or motion
sensors 217 to detect whether the ADV has contacted a lane curb
(e.g., lane shoulder, lane separator, lane warning track) and the
timing of such contacts. Based on the timing of the contacts by a
wheel or wheels of the ADV, an angle representing a difference
between a moving direction of the ADV and a lane direction of the
lane is calculated. The lane departure information concerning the
difference between the moving direction of the ADV and the lane
direction of the lane is fed back to planning module 304 and/or
control module 305. Planning module 304 and/or control module 305
may determine whether a correction action is needed based on the
lane departure information provided by lane departure detector 306.
Such a correction may be performed by control module 305.
Alternatively, planning module 304 may have to replan the route
segment for the next planning cycle in order to correct the moving
direction of the ADV. If it is determined that a correction action
is needed, a control command is generated and issued to vehicle
platform 405 to correct the moving direction of ADV.
According to one aspect of the invention, motion detector 321 of
lane departure detector 306 detects at a first point in time that a
wheel of an ADV rolls onto a lane curb disposed on an edge of a
lane in which the ADV is moving. The motion detector 321 detects at
a second point in time that the wheel of the ADV rolls off the lane
curb of the lane. The contacts between the wheel rolling onto and
rolling off the lane curb is detected using a sensor associated
with the wheel such as a tire pressure sensor or a motion sensor.
The wheel can be any one of the wheels of the ADV, either being a
front wheel or a rear wheel. Angle calculator 322 of lane departure
detector 306 calculates an angle between a moving direction of the
ADV and a lane direction of the lane based on the time difference
between the first point in time and the second point in time in
view of a current speed of the ADV. The angle information
representing the difference between the lane direction and the
moving direction is provided to planning module 304 and/or control
module 305. If the difference between the lane direction and the
moving direction of the ADV is above a predetermined threshold,
planning module 304 and/or control module 305 then generate a
control command (e.g., speed control command, steering control
command) based on the angle to adjust the moving direction of the
ADV in order to prevent the ADV from further drifting off the lane
direction of the lane.
In calculating the angle, according to one embodiment, a distance
perpendicular to the lane direction of the lane (e.g., lateral
moving distance) is calculated from the first point in time to the
second point in time. The angle is then calculated based on the
distance and the current speed of the ADV in view of the time
difference between the first point in time and the second point in
time. The distance perpendicular to the lane direction may be
calculated based on a wheel width of the wheel and a curb width of
the lane curb. The wheel width may be determined based on the
specification of the wheel. Curb width of the lane curb may be
determined based on the perception data perceiving the lane curb
such as an image of the lane curve captured by a camera.
According to another aspect of the invention, motion detector 321
detects at a first point in time that a first wheel of an ADV
contacts a lane curb disposed on an edge of a lane in which the ADV
is moving. The motion detector 321 detects at a second point in
time that a second wheel of the ADV contacts the lane curb of the
lane. The contact between the first wheel and the lane curb is
detected using a sensor associated with the first wheel such as a
tire pressure sensor or a motion sensor. The contact between the
second wheel and the lane curb is detected using a sensor
associated with the first wheel such as a tire pressure sensor or a
motion sensor. The first wheel and the second wheel can be any of
the wheels of the ADV such as a pair of front wheels or rear
wheels. Angle calculator 322 calculates an angle between a moving
direction of the ADV and a lane direction of the lane based on the
time difference between the first point in time and the second
point in time in view of a current speed of the ADV.
The angle information representing the difference between the lane
direction and the moving direction is provided to planning module
304 and/or control module 305. If the difference between the lane
direction and the moving direction of the ADV is above a
predetermined threshold, planning module 304 and/or control module
305 then generate a control command (e.g., speed control command,
steering control command) based on the angle to adjust the moving
direction of the ADV in order to prevent the ADV from further
drifting off the lane direction of the lane.
In calculating the angle, according to one embodiment, a first
distance between the first wheel and the second wheel is
determined. The first distance can be the length of an axle coupled
to the first wheel and the second wheel. A second distance that the
ADV has moved perpendicular to the lane direction of the lane is
determined based on the time difference between the first point in
time and the second point in time in view of the current speed of
the ADV. The angle is then calculated based on a sinusoidal
relationship between the first distance and the second
distance.
Note that the correction action to correct the moving direction of
the ADV may be performed by planning module 304 and/or control
module 305 dependent upon how far off the moving direction is from
the lane direction. If the difference between the lane direction
and the moving direction is significantly larger (e.g., greater
than a higher predetermined threshold), planning module 304 may
have to perform replanning; otherwise, control module 305 can
perform the correction by modifying a previous command or
generating a new command.
FIG. 5 is a diagram illustrating a typical scenario when a vehicle
contacts a lane curb. Referring to FIG. 5, when a wheel or wheels
(e.g., a pair of front wheels or rear wheels) of ADV 501 contact
lane curb 502 of lane 500, the sudden motion can be detected using
a tire pressure sensor and/or motion sensor associated with the
wheel or wheels. In addition, the timing of the contacts of the
wheel rolling on and rolling off lane curb 502 or the contacts
between the wheels of ADV 502 and lane curb 502 can be recorded.
Based on the timing of the contacts, angle 505 can be calculated
between lane direction 503 of lane 500 and moving direction 504 of
ADV 501. Angle 505 represents the difference between lane direction
503 and moving direction 504. A proper action may be performed to
correct moving direction 504 if angle 505 is greater than a
predetermined threshold. In this example, lane curb 502 is a single
piece of lane curb. Alternatively, lane curb 502 can be an array of
lane curb segments distributed along an edge of lane 500, such as,
for example, array of lane curb segments 506.
In one embodiment, whether a correction is performed may also be
dependent upon the driving circumstances or driving environment at
the point in time. For example, if lane 500 is a narrower lane or a
lane with heavy traffic, the threshold associated with angle 505 to
trigger a correction action may be lower because of a lower error
margin of lane departure. Similarly, a higher threshold may be
utilized for a wider lane or a lane with less traffic because a
higher error margin can be tolerated. Further, a lower threshold
may be applied to a two-way traffic lane and a higher threshold may
be applied to a one-way traffic lane. The rules governing the
thresholds may be determined offline by a data analytics system
(e.g., data analytics system 103) based on the driving statistics
in the past.
FIG. 6 is a diagram for determining a difference between a moving
direction and a lane direction according to one embodiment of the
invention. Referring to FIG. 6, when wheel 601 of the ADV rolls
onto a lane curb 502, such a sudden motion is detected, for
example, by a tire pressure sensor and/or a motion sensor
associated with wheel 601. The time of rolling on movement is
recorded (referred to as T1). Subsequently, when wheel 601 of the
ADV rolls off lane curb 502, the time of the rolling off movement
is recorded (referred to as T2). Based on the difference between
time T1 and T2, a lateral moving distance 605 (referred to as S)
between the contacting time T1 and T2 can be calculated in view of
a current speed (V) of the ADV: S=Vx*|T2-T1| where Vx refers to the
current speed V projected onto the X axis: Vx=V sin(.theta.). Angle
.theta. represents the angle 505 between moving direction 504 and
lane direction 503. Lateral moving distance S refers to a distance
that is perpendicular to the lane direction 503 that the ADV has
moved between T1 and T2.
On the other hand, distance S can be determined in view of wheel
width 602 of wheel 601 (referred to as W1), diameter or wheel size
604 of wheel 601 (referred to as D), and lane curb width 603
(referred to as W2) as follows: S=W1*cos(.theta.)+D*sin(.theta.)+W2
The above two equations can be combined to solve angle .theta. as
follows: W1*cos(.theta.)+D*sin(.theta.)+W2=V*sin(.theta.)|T2-T1|
When angle .theta. is small, cos(.theta.) is close to one while
sin(.theta.) is close to zero. Thus, S is approximately equal to
(W1+W2). The above equation can be simplified to solve angle
.theta. as follows: W1+W2=V*sin(.theta.)|T2-T1|
Note that wheel width W1 is known parameter that can be determined
based on the specification of wheel 601 of the ADV. Lane curb width
W2 can be estimated based on the perception data that perceives
lane curb 502. For example, an image of lane curb 502 captured by a
camera can be recognized and analyzed to determine the width of
lane curb 502.
FIG. 7 is a flow diagram illustrating a process of operating an
autonomous driving vehicle according to one embodiment of the
invention. Process 700 may be performed by processing logic which
may include software, hardware, or a combination thereof. For
example, process 700 may be performed by lane departure detector
306 of FIG. 3. Referring to FIG. 7, in operation 701, processing
logic detects at a first point in time that a wheel of an ADV rolls
onto a lane curb disposed on an edge of a driving lane in which the
ADV is moving. In operation 702, processing logic detects at a
second point in time that the same wheel of the ADV rolls off the
lane curb. Such detections can be performed using a tire pressure
sensor and/or a motion sensor associated with the wheel. In one
embodiment, each of the wheels of the ADV is associated with a tire
pressure sensor and/or a motion sensor. Such sensors may be
disposed or mounted near the corresponding wheel, for example, near
a suspension joint associated with the wheel. Thus, when a wheel of
the ADV contacts a lane curb, it can be precisely determined which
of the wheels of the ADV contacts the lane curb. It can also detect
whether the wheel is rolling onto or engaging with the lane curb or
the wheel is rolling off or disengaging from the lane curb.
In operation 703, processing logic calculates an angle between a
moving direction of the ADV and a lane direction of the lane based
on a difference between the first point in time and the second
point in time in view of a current speed of the ADV. A moving
direction of the ADV is typically perpendicular to a front axle
connecting a pair of front wheels or a rear axle connecting a pair
of rear wheels. A lane direction of the lane is typically in
parallel with a longitudinal direction of a lane curb disposed on
an edge of the lane or a longitudinal distribution pattern of an
array of lane curb segments. The angle represents the difference
between the moving direction and the lane direction, which in turn
represents how far the ADV has drifted off the lane. In operation
704, processing logic generates a control command (e.g., speed
control command, steering control command) based on the angle to
adjust the moving direction of the ADV to prevent the ADV from
further drifting off the lane direction of the lane. In one
embodiment, the control command to adjust the moving direction is
generated when the angle is above a predetermined threshold.
Note that the lane departure detection techniques described above
are based on the detection of a single wheel of the ADV rolling on
and rolling off a lane curb. According to another aspect of the
invention, the lane departure of the ADV can be detected based on
multiple wheels (e.g., pair of front wheels, pair of rear wheels,
or a front wheel and a rear wheel in combined) of the ADV
contacting a lane curb of the lane.
FIG. 8 is a diagram for determining a difference between a moving
direction and a lane direction according to another embodiment of
the invention. Referring to FIG. 8, when wheel 801 of the ADV
contacts or rolls onto lane curb 502, such a sudden motion is
detected, for example, by a tire pressure sensor and/or a motion
sensor associated with wheel 801. The time of the contact (T1) is
recorded. Subsequently, when another wheel 802 of the ADV contacts
or rolls onto lane curb 502, the time of contact (T2) is recorded.
Based on the difference between time T1 and T2, a lateral moving
distance (S) between the contacting time T1 and T2 can be
calculated in view of the current speed (V) of the ADV:
S=Vx*|T2-T1| where Vx is the current speed V projected onto X axis:
Vx=V sin(.theta.). Angle .theta. represents the angle 505 between
moving direction 504 and lane direction 503.
On the other hand, distance S can be determined in view of wheel
width (W1) of each wheel, diameter or wheel size (D) of each wheel,
and lane curb width (W2) as follows:
S=W1*cos(.theta.)+D*sin(.theta.)-W2+W*cos(.theta.) where W
represents a length of an axle connecting wheels 801-802. The above
two equations can be combined to solve angle .theta. as follows:
W1*cos(.theta.)+D*sin(.theta.)-W2+W*cos(.theta.)=V*sin(.theta.)|T2-T1|
When angle .theta. is small, cos(.theta.) is close to one while
sin(.theta.) is close to zero. Thus, S is approximately equal to
(W1+W2+W). The above equation can be simplified to solve angle
.theta. as follows: W1-W2+W=V*sin(.theta.)|T2-T1|
Note that wheel width W1 is known parameter that can be determined
based on the specification of wheel 601 of the ADV. Lane curb width
W2 can be estimated based on the perception data that perceives
lane curb 502. For example, an image of lane curb 502 captured by a
camera can be recognized and analyzed to determine the width of
lane curb 502. Similarly, the axle length W is also known based on
the specification of the ADV. In one embodiment, if W is
significantly longer or wider when W1 and W2, the above equation
can be simplified as W=V*sin(.theta.)|T2-T1|.
In this embodiment, wheels 801 and 802 are coupled to the same
axle. In one embodiment, the above techniques can be extended to
calculate the angle between the moving direction and the lane
direction based on wheels that are on difference axles such as a
front wheel and a rear wheel of the ADV. In such an embodiment, a
distance between a front axle and a rear axle (referred to herein
as R) may need to be considered if one wheel is on one side and the
other wheel is on the other side of the ADV. The lateral moving
distance S may further include distance R projected onto the X
axis: R*sin(.theta.).
Thus, if the front wheel and the rear wheel are on different sides
of the ADV, one on the driver side and the other one on the
passenger side, lateral moving distance S can be defined as
follows:
S=W1*cos(.theta.)+D*sin(.theta.)+W2+W*cos(.theta.)+R*sin(.theta.)
If angle .theta. is small, the distance R*sin(.theta.) may be
ignored for simplification in calculating the angle: S=W1+W2+W. If
the wheels are on the same side of the ADV, S can be defined as:
S=W1*cos(.theta.)+D*sin(.theta.)+W2+R*sin(.theta.) If angle .theta.
is small, the distance R*sin(.theta.) and D*sin(.theta.) may be
ignored for simplification in calculating the angle: S=W1+W2.
FIG. 9 is a flow diagram illustrating a process of operating an
autonomous driving vehicle according to another embodiment of the
invention. Process 900 may be performed by processing logic which
may include software, hardware, or a combination thereof. For
example, process 900 may be performed by lane departure detector
306 of FIG. 3. Referring to FIG. 9, in operation 901, processing
logic detects at a first point in time that a first wheel (e.g., a
right front wheel or a right rear wheel) of an ADV contacts a lane
curb (e.g., lane shoulder, lane separator, lane warning track) of a
lane in which the ADV is moving. The lane curb is disposed on an
edge of the lane or between lanes. In operation 902, processing
logic detects at a second point in time that a second wheel of the
ADV (e.g., a left front wheel or a left rear wheel) contacts the
lane curb. In operation 903, processing logic calculates an angle
between a moving direction of the ADV and a lane direction of the
lane based on the first point in time and the second point in time
in view of a current speed of the ADV. In operation 904, processing
logic generates a control command to adjust the moving direction of
the ADV based on the calculated angle, such that the ADV remains
within the lane according to the lane direction of the lane.
Note that some or all of the components as shown and described
above may be implemented in software, hardware, or a combination
thereof. For example, such components can be implemented as
software installed and stored in a persistent storage device, which
can be loaded and executed in a memory by a processor (not shown)
to carry out the processes or operations described throughout this
application. Alternatively, such components can be implemented as
executable code programmed or embedded into dedicated hardware such
as an integrated circuit (e.g., an application specific IC or
ASIC), a digital signal processor (DSP), or a field programmable
gate array (FPGA), which can be accessed via a corresponding driver
and/or operating system from an application. Furthermore, such
components can be implemented as specific hardware logic in a
processor or processor core as part of an instruction set
accessible by a software component via one or more specific
instructions.
FIG. 10 is a block diagram illustrating an example of a data
processing system which may be used with one embodiment of the
invention. For example, system 1500 may represent any of data
processing systems described above performing any of the processes
or methods described above, such as, for example, data processing
system 110 or any of servers 103-104 of FIG. 1. System 1500 can
include many different components. These components can be
implemented as integrated circuits (ICs), portions thereof,
discrete electronic devices, or other modules adapted to a circuit
board such as a motherboard or add-in card of the computer system,
or as components otherwise incorporated within a chassis of the
computer system.
Note also that system 1500 is intended to show a high level view of
many components of the computer system. However, it is to be
understood that additional components may be present in certain
implementations and furthermore, different arrangement of the
components shown may occur in other implementations. System 1500
may represent a desktop, a laptop, a tablet, a server, a mobile
phone, a media player, a personal digital assistant (PDA), a
Smartwatch, a personal communicator, a gaming device, a network
router or hub, a wireless access point (AP) or repeater, a set-top
box, or a combination thereof. Further, while only a single machine
or system is illustrated, the term "machine" or "system" shall also
be taken to include any collection of machines or systems that
individually or jointly execute a set (or multiple sets) of
instructions to perform any one or more of the methodologies
discussed herein.
In one embodiment, system 1500 includes processor 1501, memory
1503, and devices 1505-1508 connected via a bus or an interconnect
1510. Processor 1501 may represent a single processor or multiple
processors with a single processor core or multiple processor cores
included therein. Processor 1501 may represent one or more
general-purpose processors such as a microprocessor, a central
processing unit (CPU), or the like. More particularly, processor
1501 may be a complex instruction set computing (CISC)
microprocessor, reduced instruction set computing (RISC)
microprocessor, very long instruction word (VLIW) microprocessor,
or processor implementing other instruction sets, or processors
implementing a combination of instruction sets. Processor 1501 may
also be one or more special-purpose processors such as an
application specific integrated circuit (ASIC), a cellular or
baseband processor, a field programmable gate array (FPGA), a
digital signal processor (DSP), a network processor, a graphics
processor, a communications processor, a cryptographic processor, a
co-processor, an embedded processor, or any other type of logic
capable of processing instructions.
Processor 1501, which may be a low power multi-core processor
socket such as an ultra-low voltage processor, may act as a main
processing unit and central hub for communication with the various
components of the system. Such processor can be implemented as a
system on chip (SoC). Processor 1501 is configured to execute
instructions for performing the operations and steps discussed
herein. System 1500 may further include a graphics interface that
communicates with optional graphics subsystem 1504, which may
include a display controller, a graphics processor, and/or a
display device.
Processor 1501 may communicate with memory 1503, which in one
embodiment can be implemented via multiple memory devices to
provide for a given amount of system memory. Memory 1503 may
include one or more volatile storage (or memory) devices such as
random access memory (RAM), dynamic RAM (DRAM), synchronous DRAM
(SDRAM), static RAM (SRAM), or other types of storage devices.
Memory 1503 may store information including sequences of
instructions that are executed by processor 1501, or any other
device. For example, executable code and/or data of a variety of
operating systems, device drivers, firmware (e.g., input output
basic system or BIOS), and/or applications can be loaded in memory
1503 and executed by processor 1501. An operating system can be any
kind of operating systems, such as, for example, Robot Operating
System (ROS), Windows.RTM. operating system from Microsoft.RTM.,
Mac OS.RTM./iOS.RTM. from Apple, Android.RTM. from Google.RTM.,
LINUX, UNIX, or other real-time or embedded operating systems.
System 1500 may further include IO devices such as devices
1505-1508, including network interface device(s) 1505, optional
input device(s) 1506, and other optional IO device(s) 1507. Network
interface device 1505 may include a wireless transceiver and/or a
network interface card (NIC). The wireless transceiver may be a
WiFi transceiver, an infrared transceiver, a Bluetooth transceiver,
a WiMax transceiver, a wireless cellular telephony transceiver, a
satellite transceiver (e.g., a global positioning system (GPS)
transceiver), or other radio frequency (RF) transceivers, or a
combination thereof. The NIC may be an Ethernet card.
Input device(s) 1506 may include a mouse, a touch pad, a touch
sensitive screen (which may be integrated with display device
1504), a pointer device such as a stylus, and/or a keyboard (e.g.,
physical keyboard or a virtual keyboard displayed as part of a
touch sensitive screen). For example, input device 1506 may include
a touch screen controller coupled to a touch screen. The touch
screen and touch screen controller can, for example, detect contact
and movement or break thereof using any of a plurality of touch
sensitivity technologies, including but not limited to capacitive,
resistive, infrared, and surface acoustic wave technologies, as
well as other proximity sensor arrays or other elements for
determining one or more points of contact with the touch
screen.
IO devices 1507 may include an audio device. An audio device may
include a speaker and/or a microphone to facilitate voice-enabled
functions, such as voice recognition, voice replication, digital
recording, and/or telephony functions. Other IO devices 1507 may
further include universal serial bus (USB) port(s), parallel
port(s), serial port(s), a printer, a network interface, a bus
bridge (e.g., a PCI-PCI bridge), sensor(s) (e.g., a motion sensor
such as an accelerometer, gyroscope, a magnetometer, a light
sensor, compass, a proximity sensor, etc.), or a combination
thereof. Devices 1507 may further include an imaging processing
subsystem (e.g., a camera), which may include an optical sensor,
such as a charged coupled device (CCD) or a complementary
metal-oxide semiconductor (CMOS) optical sensor, utilized to
facilitate camera functions, such as recording photographs and
video clips. Certain sensors may be coupled to interconnect 1510
via a sensor hub (not shown), while other devices such as a
keyboard or thermal sensor may be controlled by an embedded
controller (not shown), dependent upon the specific configuration
or design of system 1500.
To provide for persistent storage of information such as data,
applications, one or more operating systems and so forth, a mass
storage (not shown) may also couple to processor 1501. In various
embodiments, to enable a thinner and lighter system design as well
as to improve system responsiveness, this mass storage may be
implemented via a solid state device (SSD). However in other
embodiments, the mass storage may primarily be implemented using a
hard disk drive (HDD) with a smaller amount of SSD storage to act
as a SSD cache to enable non-volatile storage of context state and
other such information during power down events so that a fast
power up can occur on re-initiation of system activities. Also a
flash device may be coupled to processor 1501, e.g., via a serial
peripheral interface (SPI). This flash device may provide for
non-volatile storage of system software, including BIOS as well as
other firmware of the system.
Storage device 1508 may include computer-accessible storage medium
1509 (also known as a machine-readable storage medium or a
computer-readable medium) on which is stored one or more sets of
instructions or software (e.g., module, unit, and/or logic 1528)
embodying any one or more of the methodologies or functions
described herein. Processing module/unit/logic 1528 may represent
any of the components described above, such as, for example,
planning module 304, control module 305, and/or lane departure
detector 306 of FIG. 3. Processing module/unit/logic 1528 may also
reside, completely or at least partially, within memory 1503 and/or
within processor 1501 during execution thereof by data processing
system 1500, memory 1503 and processor 1501 also constituting
machine-accessible storage media. Processing module/unit/logic 1528
may further be transmitted or received over a network via network
interface device 1505.
Computer-readable storage medium 1509 may also be used to store the
some software functionalities described above persistently. While
computer-readable storage medium 1509 is shown in an exemplary
embodiment to be a single medium, the term "computer-readable
storage medium" should be taken to include a single medium or
multiple media (e.g., a centralized or distributed database, and/or
associated caches and servers) that store the one or more sets of
instructions. The terms "computer-readable storage medium" shall
also be taken to include any medium that is capable of storing or
encoding a set of instructions for execution by the machine and
that cause the machine to perform any one or more of the
methodologies of the present invention. The term "computer-readable
storage medium" shall accordingly be taken to include, but not be
limited to, solid-state memories, and optical and magnetic media,
or any other non-transitory machine-readable medium.
Processing module/unit/logic 1528, components and other features
described herein can be implemented as discrete hardware components
or integrated in the functionality of hardware components such as
ASICS, FPGAs, DSPs or similar devices. In addition, processing
module/unit/logic 1528 can be implemented as firmware or functional
circuitry within hardware devices. Further, processing
module/unit/logic 1528 can be implemented in any combination
hardware devices and software components.
Note that while system 1500 is illustrated with various components
of a data processing system, it is not intended to represent any
particular architecture or manner of interconnecting the
components; as such details are not germane to embodiments of the
present invention. It will also be appreciated that network
computers, handheld computers, mobile phones, servers, and/or other
data processing systems which have fewer components or perhaps more
components may also be used with embodiments of the invention.
Some portions of the preceding detailed descriptions have been
presented in terms of algorithms and symbolic representations of
operations on data bits within a computer memory. These algorithmic
descriptions and representations are the ways used by those skilled
in the data processing arts to most effectively convey the
substance of their work to others skilled in the art. An algorithm
is here, and generally, conceived to be a self-consistent sequence
of operations leading to a desired result. The operations are those
requiring physical manipulations of physical quantities.
It should be borne in mind, however, that all of these and similar
terms are to be associated with the appropriate physical quantities
and are merely convenient labels applied to these quantities.
Unless specifically stated otherwise as apparent from the above
discussion, it is appreciated that throughout the description,
discussions utilizing terms such as those set forth in the claims
below, refer to the action and processes of a computer system, or
similar electronic computing device, that manipulates and
transforms data represented as physical (electronic) quantities
within the computer system's registers and memories into other data
similarly represented as physical quantities within the computer
system memories or registers or other such information storage,
transmission or display devices.
Embodiments of the invention also relate to an apparatus for
performing the operations herein. Such a computer program is stored
in a non-transitory computer readable medium. A machine-readable
medium includes any mechanism for storing information in a form
readable by a machine (e.g., a computer). For example, a
machine-readable (e.g., computer-readable) medium includes a
machine (e.g., a computer) readable storage medium (e.g., read only
memory ("ROM"), random access memory ("RAM"), magnetic disk storage
media, optical storage media, flash memory devices).
The processes or methods depicted in the preceding figures may be
performed by processing logic that comprises hardware (e.g.
circuitry, dedicated logic, etc.), software (e.g., embodied on a
non-transitory computer readable medium), or a combination of both.
Although the processes or methods are described above in terms of
some sequential operations, it should be appreciated that some of
the operations described may be performed in a different order.
Moreover, some operations may be performed in parallel rather than
sequentially.
Embodiments of the present invention are not described with
reference to any particular programming language. It will be
appreciated that a variety of programming languages may be used to
implement the teachings of embodiments of the invention as
described herein.
In the foregoing specification, embodiments of the invention have
been described with reference to specific exemplary embodiments
thereof. It will be evident that various modifications may be made
thereto without departing from the broader spirit and scope of the
invention as set forth in the following claims. The specification
and drawings are, accordingly, to be regarded in an illustrative
sense rather than a restrictive sense.
* * * * *